Working Results2

Summary Statistics

Species Information

##           y      ymin      ymax
## 1 0.4225771 0.3627116 0.4824426

Species tables and tree tables

## Parsed with column specification:
## cols(
##   Species = col_character(),
##   `Scientific Name` = col_character(),
##   `Func. Group` = col_character(),
##   Sightings = col_double(),
##   Ingestions = col_double(),
##   Removals = col_double(),
##   Nibbles = col_double(),
##   `Avg. Vistitation Rate` = col_double(),
##   `Avg. Fruit Removal Rate` = col_double(),
##   SDE = col_double(),
##   Class = col_character()
## )

##  [1] "Species"                 "Scientific Name"        
##  [3] "Func. Group"             "Sightings"              
##  [5] "Ingestions"              "Removals"               
##  [7] "Nibbles"                 "Avg. Vistitation Rate"  
##  [9] "Avg. Fruit Removal Rate" "SDE"                    
## [11] "Class"

## # A tibble: 2 x 4
##   Height count   mean     sd
##   <fct>  <int>  <dbl>  <dbl>
## 1 high      25 0.301  0.387 
## 2 low       45 0.0516 0.0580

##          TH Tree Height     visits fruit.rem.rate         SDE
## 1   258_low  258    low 0.36210317    0.150000000 0.054315476
## 2  258_high  258   high 1.01686508    0.524450549 0.533295450
## 3    13_low   13    low 1.25000000    0.153005464 0.191256831
## 4   13_high   13   high 0.77380952    0.634146341 0.490708479
## 5    18_low   18    low 0.20568783    0.006410256 0.001318512
## 6    79_low   79    low 0.76315438    0.153846154 0.117408366
## 7   79_high   79   high 2.00983045    0.559900109 1.125304287
## 8  250_high  250   high 0.00000000    0.000000000 0.000000000
## 9  388_high  388   high 1.59523810    0.806991774 1.287344021
## 10  388_low  388    low 0.51785714    0.054347826 0.028144410
## 11  406_low  406    low 1.31944444    0.098039216 0.129357298
## 12 406_high  406   high 0.62500000    0.571428571 0.357142857
## 13  200_low  200    low 1.51515152    0.115942029 0.175669741
## 14  203_low  203    low 2.17532468    0.066798523 0.145308476
## 15   6_high    6   high 0.19439935    0.702077922 0.136483492
## 16    6_low    6    low 0.11842324    0.080536312 0.009537371
## 17   75_low   75    low 0.15625000    0.000000000 0.000000000
## 18 203_high  203   high 0.25595238    0.731884058 0.187327467
## 19  90_high   90   high 0.42981902    0.561310976 0.241262135
## 20  205_low  205    low 0.24122807    0.136363636 0.032894737
## 21  25_high   25   high 0.03472222    1.000000000 0.034722222
## 22   41_low   41    low 0.00000000    0.000000000 0.000000000
## 23  41_high   41   high 0.28645833    0.714285714 0.204613095
## 24   92_low   92    low 0.28905508    0.070530733 0.020387267
## 25  67_high   67   high 0.37500000    1.000000000 0.375000000
## 26  262_low  262    low 0.06410256    0.000000000 0.000000000
## 27 293_high  293   high 0.00000000    0.000000000 0.000000000
## 28    8_low    8    low 0.07575758    0.000000000 0.000000000
## 29   19_low   19    low 0.00000000    0.000000000 0.000000000
## 30  144_low  144    low 0.64406318    0.177388836 0.114249618
## 31 160_high  160   high 0.00000000    0.000000000 0.000000000
## 32   90_low   90    low 0.09572218    0.190476190 0.018232796
## 33 138_high  138   high 0.35054200    0.456794294 0.160125586
## 34  138_low  138    low 0.32778922    0.258405694 0.084702600
## 35  127_low  127    low 0.07502914    0.104761905 0.007860195
## 36 127_high  127   high 0.06944444    0.750000000 0.052083333
## 37   60_low   60    low 0.00000000    0.000000000 0.000000000
## 38  82_high   82   high 1.64368964    0.769627660 1.265029014
## 39   82_low   82    low 0.72448385    0.253885148 0.183935689
## 40  107_low  107    low 0.06578947    0.666666667 0.043859649
## 41  121_low  121    low 0.05341880    0.046875000 0.002504006
## 42 121_high  121   high 0.00000000    0.000000000 0.000000000
## 43  141_low  141    low 0.30102710    0.256465517 0.077203070
## 44  160_low  160    low 0.49533800    0.106162431 0.052586286
## 45   23_low   23    low 0.04941239    0.175000000 0.008647169
## 46 399_high  399   high 0.23103632    0.159663866 0.036888153
## 47  399_low  399    low 0.23698524    0.303921569 0.072024925
## 48   84_low   84    low 0.20834691    0.120035703 0.025009067
## 49  134_low  134    low 0.07470539    0.128205128 0.009577614
## 50  384_low  384    low 0.24621212    0.416666667 0.102588384
## 51  84_high   84   high 0.00000000    0.000000000 0.000000000
## 52 197_high  197   high 0.72916667    0.565217391 0.412137681
## 53  197_low  197    low 0.78125000    0.026666667 0.020833333
## 54   46_low   46    low 0.45454545    0.064814815 0.029461279
## 55   53_low   53    low 0.11363636    0.000000000 0.000000000
## 56 129_high  129   high 0.00000000    0.000000000 0.000000000
## 57  129_low  129    low 0.57849702    0.090425532 0.052310901
## 58   17_low   17    low 0.41666667    0.070422535 0.029342723
## 59   54_low   54    low 0.23674242    0.169706180 0.040176653
## 60   89_low   89    low 0.07352941    0.120000000 0.008823529
## 61 295_high  295   high 0.96590909    0.424640400 0.410164023
## 62  295_low  295    low 0.48413826    0.225877193 0.109355791
## 63   83_low   83    low 1.10294118    0.133858268 0.147637795
## 64  92_high   92   high 0.19943020    0.358333333 0.071462488
## 65   97_low   97    low 0.05208333    0.000000000 0.000000000
## 66  269_low  269    low 0.06410256    0.500000000 0.032051282
## 67   26_low   26    low 0.00000000    0.000000000 0.000000000
## 68   72_low   72    low 0.49242424    0.288888889 0.142255892
## 69  72_high   72   high 0.29166667    0.500000000 0.145833333
## 70  265_low  265    low 0.00000000    0.000000000 0.000000000

## 
##  Kruskal-Wallis rank sum test
## 
## data:  SDE by Height
## Kruskal-Wallis chi-squared = 8.7213, df = 1, p-value = 0.003145

##             Df Sum Sq Mean Sq F value   Pr(>F)    
## Height       1  1.000  1.0005   18.15 6.43e-05 ***
## Residuals   68  3.748  0.0551                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## Loading required package: carData

## Registered S3 methods overwritten by 'car':
##   method                          from
##   influence.merMod                lme4
##   cooks.distance.influence.merMod lme4
##   dfbeta.influence.merMod         lme4
##   dfbetas.influence.merMod        lme4

## 
## Attaching package: 'car'

## The following object is masked from 'package:boot':
## 
##     logit

## The following object is masked from 'package:psych':
## 
##     logit

## The following object is masked from 'package:purrr':
## 
##     some

## The following object is masked from 'package:dplyr':
## 
##     recode

## Levene's Test for Homogeneity of Variance (center = median)
##       Df F value    Pr(>F)    
## group  1  19.494 3.705e-05 ***
##       68                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Checking significance in differences of SDE

## Parsed with column specification:
## cols(
##   Species = col_character(),
##   `Scientific Name` = col_character(),
##   `Func. Group` = col_character(),
##   Sightings = col_double(),
##   Ingestions = col_double(),
##   Removals = col_double(),
##   Nibbles = col_double(),
##   `Avg. Vistitation Rate` = col_double(),
##   `Avg. Fruit Removal Rate` = col_double(),
##   SDE = col_double(),
##   Class = col_character()
## )

## file saved to Table3.png

## file saved to SPPtable.pdf

## Note that HTML color may not be displayed on PDF properly.

## [1] "FD"  "NFD"

## # A tibble: 2 x 4
##   `Func. Group` count  mean    sd
##   <fct>         <int> <dbl> <dbl>
## 1 FD                7 2.14  2.05 
## 2 NFD              13 0.446 0.454

## # A tibble: 20 x 11
##    Species `Scientific Nam… `Func. Group` Sightings Ingestions Removals Nibbles
##    <chr>   <chr>            <fct>             <dbl>      <dbl>    <dbl>   <dbl>
##  1 Centra… "Dasyprocta pun… NFD                 260          0       44      70
##  2 Brown … "Metachirus nud… NFD                  82          2        2       4
##  3 Baudó … "Penelope orton… FD                   20          2        4       2
##  4 Choco … "Ramphastos bre… FD                  272         99      142       3
##  5 Chestn… "Ramphastos amb… FD                  316        157      168       1
##  6 South … "Nasua nasua "   NFD                 458          2        0     175
##  7 Collar… "Pecari tajacu"  NFD                 136          4        0       0
##  8 Kinkaj… "Potos flavus"   NFD                  84          0        3      14
##  9 Oilbird "Steatornis car… FD                   73          0       36       0
## 10 Common… "Didelphis mars… NFD                 100          1        0      27
## 11 Lowlan… "Cuniculus paca" NFD                 416          0       31      73
## 12 Rufous… "Odontophorus e… NFD                 434          0        0       4
## 13 Rodent… ""               NFD                1380          0      197      45
## 14 Rufous… "Diplomys labil… NFD                   5          0        0       1
## 15 Southe… "Amazona farino… FD                   10          0        7       0
## 16 Squirr… ""               NFD                 675          0      249     109
## 17 Toucan… ""               FD                  284         75       85       0
## 18 Tome's… "Proechimys sem… NFD                 136          0       24       1
## 19 Long-w… "Cephalopterus … FD                  269         34       96       2
## 20 Brown … "Aramides wolfi" NFD                 127          0        0       7
## # … with 4 more variables: `Avg. Vistitation Rate` <dbl>, `Avg. Fruit Removal
## #   Rate` <dbl>, SDE <dbl>, Class <chr>

## 
##  One-way analysis of means (not assuming equal variances)
## 
## data:  SDE and Func_group
## F = 4.641, num df = 1.0000, denom df = 6.3171, p-value = 0.07238

## 
##  Kruskal-Wallis rank sum test
## 
## data:  SDE by Func_group
## Kruskal-Wallis chi-squared = 5.3009, df = 1, p-value = 0.02131

##             Df Sum Sq Mean Sq F value  Pr(>F)   
## Func_group   1  13.05  13.054   8.462 0.00936 **
## Residuals   18  27.77   1.543                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = SDE ~ Func_group, data = spptable)
## 
## $Func_group
##             diff       lwr        upr     p adj
## NFD-FD -1.693846 -2.917178 -0.4705144 0.0093621

##             Df Sum Sq Mean Sq F value Pr(>F)  
## Class        1   6.40   6.401   3.347 0.0839 .
## Residuals   18  34.42   1.912                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = SDE ~ Class, data = spptable)
## 
## $Class
##                  diff       lwr       upr     p adj
## Mammal-Bird -1.137172 -2.443006 0.1686627 0.0839294

## Levene's Test for Homogeneity of Variance (center = median)
##       Df F value  Pr(>F)  
## group  1  6.2905 0.02194 *
##       18                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## Levene's Test for Homogeneity of Variance (center = median)
##       Df F value  Pr(>F)  
## group  1  5.2885 0.03365 *
##       18                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Visualization of ht vs focal 50

## `geom_smooth()` using formula 'y ~ x'

## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'

Correlation at each band

effect of height focal against visitation

##    Tree Height Month Year           Species   utm1  utm2 ele Visitation
## 16   79   high    10 2016 Oenocarpus bataua 644127 38676 540        189
##    Richness M.Richness B.Richness T.Ingestions T.Removal T.Nibble
## 16        5          0          5           57       117        0
##    Avg.synch.neighbors Lek. DAP_CENSUS_1 ALTURA_CENSUS_1 NOTAS_CENSUS_1
## 16                  NA LEK1         29.5              21             NA
##    TIPO_DE_BOSQUE_COLLECTION DOSEL_CENSUS_1 CANOPY_DENS_CENSUS_1
## 16                Secundario             21                91.42
##    ARB_DAP10_CENSUS_1 ARB_DAP50_CENSUS_1 CERCROPIA_CENSUS_1 MICONIA_CENSUS_1
## 16                  9                  0                  0                0
##    JUV_CENSUS_1 JUV_DENS_CENSUS_1 PLANTULA_CENSUS_1 PLANTULA_DENS_CENSUS_1
## 16            4        0.05092958                41               2.088113
##    m.visitation b.visitation real.visitation real.m.visitation
## 16            0          189             189                 0
##    real.b.visitation real.richness real.m.richness real.b.richness  date
## 16               189             5               0               5 42644
##    focalmonth..50 focalmonth..100 focalmonth..150 focalmonth..200
## 16              2               3               6               9
##    focalmonth..250 focalmonth..300 focalmonth..350 focalmonth..400
## 16              10              12              15              16
##    focalmonth..450 focalmonth..500 TD  FD NRFM NFM NRFG NFG   start      end
## 16              16              16  0 189    1   d    1   e 10/5/16 10/16/16
##    days vis.rate n50 n100 n150 n200 n250 n300 n350 n400 n450 n500      Dates
## 16   11 17.18182   5   15   37   61   81  114  140  152  160  173 2016-10-01
##     FD.rate TD.rate
## 16 17.18182       0

Models 50 m band

Models using functional groups

Model Comparisons

Model 1: Original

## real.visitation ~ focalmonth..50 * Height + offset(lograte) + 
##     (1 + focalmonth..50 | Tree)

	Estimate	Std. Error	z value	Pr(>|z|)
(Intercept)	0.0405307	0.1886986	0.2147905	0.8299306
focalmonth..50	0.5152201	0.2956979	1.7423871	0.0814407
Heightlow	0.7238617	0.0437088	16.5610200	0.0000000
focalmonth..50:Heightlow	-0.2752461	0.0374950	-7.3408839	0.0000000

## real.visitation ~ focalmonth..450 * Height + offset(lograte) + 
##     (1 + focalmonth..450 | Tree)

	Estimate	Std. Error	z value	Pr(>|z|)
(Intercept)	0.2234468	0.3520308	0.6347365	0.5256003
focalmonth..450	-0.0047342	0.0302651	-0.1564254	0.8756977
Heightlow	0.4050716	0.0852282	4.7527900	0.0000020
focalmonth..450:Heightlow	0.0042980	0.0059529	0.7220041	0.4702920

## Parsed with column specification:
## cols(
##   X1 = col_character(),
##   `z/tau value` = col_double(),
##   `±SE` = col_double(),
##   `p value` = col_double()
## )

## file saved to TableGLMM2.pdf

Model 2: Trees without any visits from Dispersers are removed

## real.visitation ~ focalmonth..50 * Height + offset(lograte) + 
##     (1 + focalmonth..50 | Tree)

	Estimate	Std. Error	z value	Pr(>|z|)
(Intercept)	0.0415496	0.1880433	0.2209575	0.8251255
focalmonth..50	0.5152245	0.2960341	1.7404230	0.0817848
Heightlow	0.7235774	0.0436765	16.5667539	0.0000000
focalmonth..50:Heightlow	-0.2750984	0.0374846	-7.3389674	0.0000000

Results are qualitatively similar.

Model 3: Original amount of trees, but fruiting neighborhood is now binary

## real.visitation ~ bin50 * Height + offset(lograte) + (1 + bin50 | 
##     Tree)

	Estimate	Std. Error	z value	Pr(>|z|)
(Intercept)	0.0439227	0.1998483	0.219780	0.8260425
bin501	0.5713877	0.3322725	1.719636	0.0854987
Heightlow	0.6624992	0.0483892	13.691042	0.0000000
bin501:Heightlow	-0.3084512	0.0692597	-4.453547	0.0000084

Again, binarizing is qualitatively similar.

Model 4: Original amount of trees, but fruiting neighborhood categorized into 0,1,2,3+

## real.visitation ~ bin50 * Height + offset(lograte) + (1 + bin50 | 
##     Tree)

	Estimate	Std. Error	z value	Pr(>|z|)
(Intercept)	0.0276333	0.1891375	0.1461019	0.8838409
bin50	0.5471455	0.2952029	1.8534560	0.0638170
Heightlow	0.7421483	0.0450024	16.4913175	0.0000000
bin50:Heightlow	-0.3186904	0.0420803	-7.5733801	0.0000000

Model 5: Original data but tree with outlier in visitation in high cameras removed

## real.visitation ~ focalmonth..50 * Height + offset(lograte) + 
##     (1 + focalmonth..50 | Tree)

	Estimate	Std. Error	z value	Pr(>|z|)
(Intercept)	0.0650785	0.1908849	0.3409307	0.7331558
focalmonth..50	0.3663191	0.3096886	1.1828626	0.2368636
Heightlow	0.6933769	0.0439534	15.7752735	0.0000000
focalmonth..50:Heightlow	-0.0700859	0.0400875	-1.7483212	0.0804084

Focalmonth..50 and the interaction term are no longer significant.

Model 6: Trees without any visits from Dispersers are removed AND outlier in visitation in high cameras removed

## real.visitation ~ focalmonth..50 * Height + offset(lograte) + 
##     (1 + focalmonth..50 | Tree)

	Estimate	Std. Error	z value	Pr(>|z|)
(Intercept)	0.0657147	0.1905568	0.3448563	0.7302024
focalmonth..50	0.3664713	0.3096947	1.1833307	0.2366781
Heightlow	0.6931354	0.0439257	15.7797227	0.0000000
focalmonth..50:Heightlow	-0.0699615	0.0400800	-1.7455466	0.0808898

Results are qualitatively similar again when trees with 0 disperser visitation is removed

Checking SDE on the tree level

## file saved to euptcvm.pdf

Network Analysis

## specialisation asymmetry                       H2 
##                0.1895788                0.4342014

## weighted.cluster.coefficient.HL weighted.cluster.coefficient.LL 
##                       0.8235261                       0.9700709 
##                   generality.HL                vulnerability.LL 
##                      12.9410832                       3.5444669

## specialisation asymmetry                       H2 
##               0.09440097               0.35740596

## weighted.cluster.coefficient.HL weighted.cluster.coefficient.LL 
##                       0.7975048                       0.9144969 
##                   generality.HL                vulnerability.LL 
##                       6.0560145                       3.7946460

Correlation between Fruiting neighborhood (FN) at 50m and for FN at whole study plot

## 
##  Shapiro-Wilk normality test
## 
## data:  dummy$fn1400
## W = 0.93961, p-value = 4.615e-06

## 
##  Shapiro-Wilk normality test
## 
## data:  dummy$FN50
## W = 0.69195, p-value < 2.2e-16

Correlation between fruiting neighborhood at 50 m and entire plot.

statistic	p.value	kendall_score	denominator	var_kendall_score
0.1360697	0.0378435	1118	8216.378	289349.8

Model 1: total rate ~ FN50 + FNTotal + Camera height

	Visitation rate with random intercept			Visitation rate with random intercept AND SLOPE
Predictors	Incidence Rate Ratios	CI	p	Incidence Rate Ratios	CI	p
(Intercept)	0.60	0.43 – 0.84	0.003	0.43	0.29 – 0.62	<0.001
FN50	0.97	0.88 – 1.07	0.548	1.79	1.31 – 2.45	<0.001
Height [low]	0.71	0.62 – 0.81	<0.001	0.80	0.70 – 0.92	0.001
fn1400	1.04	1.02 – 1.05	<0.001	1.04	1.03 – 1.06	<0.001
FN50 * Height [low]	0.77	0.70 – 0.86	<0.001	0.68	0.60 – 0.77	<0.001
Zero-Inflated Model
(Intercept)	0.36	0.23 – 0.57	<0.001	0.35	0.23 – 0.56	<0.001
FN50	0.87	0.59 – 1.27	0.467	0.89	0.60 – 1.32	0.561
Random Effects
σ2	0.72			0.68
τ00	0.75 Tree			0.94 Tree
τ11				0.35 Tree.FN50
ρ01				-0.84 Tree
ICC	0.51			0.54
N	47 Tree			47 Tree
Observations	151			151
Marginal R2 / Conditional R2	0.097 / 0.558			0.181 / 0.627

Model 2: Fd rate ~ FN50 + FNTotal + Camera height

	Visitation rate with random intercept			Visitation rate with random intercept AND SLOPE			Flying visitation rate with random intercept
Predictors	Incidence Rate Ratios	CI	p	Incidence Rate Ratios	CI	p	Incidence Rate Ratios	CI	p
(Intercept)	0.60	0.43 – 0.84	0.003	0.43	0.29 – 0.62	<0.001	0.34	0.16 – 0.73	0.005
FN50	0.97	0.88 – 1.07	0.548	1.79	1.31 – 2.45	<0.001	0.72	0.61 – 0.86	<0.001
Height [low]	0.71	0.62 – 0.81	<0.001	0.80	0.70 – 0.92	0.001	0.15	0.09 – 0.24	<0.001
fn1400	1.04	1.02 – 1.05	<0.001	1.04	1.03 – 1.06	<0.001	1.07	1.04 – 1.10	<0.001
FN50 * Height [low]	0.77	0.70 – 0.86	<0.001	0.68	0.60 – 0.77	<0.001	0.12	0.03 – 0.42	0.001
Zero-Inflated Model
(Intercept)	0.36	0.23 – 0.57	<0.001	0.35	0.23 – 0.56	<0.001	1.04	0.67 – 1.60	0.877
FN50	0.87	0.59 – 1.27	0.467	0.89	0.60 – 1.32	0.561	0.80	0.44 – 1.44	0.452
Random Effects
σ2	0.72			0.68			1.71
τ00	0.75 Tree			0.94 Tree			2.06 Tree
τ11				0.35 Tree.FN50
ρ01				-0.84 Tree
ICC	0.51			0.54			0.55
N	47 Tree			47 Tree			47 Tree
Observations	151			151			151
Marginal R2 / Conditional R2	0.097 / 0.558			0.181 / 0.627			0.639 / 0.836

Model 3: Non Flying d rate ~ FN50 + FNTotal + Camera height

	Visitation rate with random intercept			Visitation rate with random intercept AND SLOPE			Flying visitation rate with random intercept			Non-Flying visitation rate with random intercept
Predictors	Incidence Rate Ratios	CI	p	Incidence Rate Ratios	CI	p	Incidence Rate Ratios	CI	p	Incidence Rate Ratios	CI	p
(Intercept)	0.60	0.43 – 0.84	0.003	0.43	0.29 – 0.62	<0.001	0.34	0.16 – 0.73	0.005	0.46	0.25 – 0.85	0.013
FN50	0.97	0.88 – 1.07	0.548	1.79	1.31 – 2.45	<0.001	0.72	0.61 – 0.86	<0.001	1.13	0.94 – 1.35	0.208
Height [low]	0.71	0.62 – 0.81	<0.001	0.80	0.70 – 0.92	0.001	0.15	0.09 – 0.24	<0.001	1.68	1.20 – 2.36	0.003
fn1400	1.04	1.02 – 1.05	<0.001	1.04	1.03 – 1.06	<0.001	1.07	1.04 – 1.10	<0.001	1.00	0.98 – 1.02	0.835
FN50 * Height [low]	0.77	0.70 – 0.86	<0.001	0.68	0.60 – 0.77	<0.001	0.12	0.03 – 0.42	0.001	0.79	0.66 – 0.95	0.013
Zero-Inflated Model
(Intercept)	0.36	0.23 – 0.57	<0.001	0.35	0.23 – 0.56	<0.001	1.04	0.67 – 1.60	0.877	0.90	0.59 – 1.38	0.634
FN50	0.87	0.59 – 1.27	0.467	0.89	0.60 – 1.32	0.561	0.80	0.44 – 1.44	0.452	0.81	0.58 – 1.15	0.240
Random Effects
σ2	0.72			0.68			1.71			0.51
τ00	0.75 Tree			0.94 Tree			2.06 Tree			0.74 Tree
τ11				0.35 Tree.FN50
ρ01				-0.84 Tree
ICC	0.51			0.54			0.55			0.59
N	47 Tree			47 Tree			47 Tree			47 Tree
Observations	151			151			151			151
Marginal R2 / Conditional R2	0.097 / 0.558			0.181 / 0.627			0.639 / 0.836			0.036 / 0.605